1. Centre for Computational Science, University College London
GRID Middleware for Biomolecular Science Applications: A User’s Perspective
Kashif Sadiq
Centre for Computational Science, University College London

2. Overview Scientific Motivation Computational Techniques
HIV-1 Protease
Computational Techniques
Ensemble MD
Thermodynamic Integration
Computational Requirements
HPC Requirements
Middleware Requirements: MOCAS
Conclusion

3. HIV-1 Protease Enzyme of HIV responsible for protein maturation
Monomer B
Monomer A
1 - 99
Enzyme of HIV responsible for protein maturation
Target for Anti-retroviral Inhibitors
Example of Structure Assisted Drug Design
8 FDA inhibitors of HIV-1 protease
Flaps
Glycine - 48, 148
Saquinavir
So what’s the problem ?
P2 Subsite
Catalytic Aspartic Acids - 25, 125
Emergence of drug resistant mutations in protease
Render drug ineffective
Drug Resistant mutants have emerged for all FDA inhibitors
Leucine - 90, 190
C-terminal
N-terminal

4. Molecular Dynamics Simulations of HIV-1 Protease
AIMS
Study the differential interactions between wild-type and mutant proteases with an inhibitor
Gain insight at molecular level into dynamical cause of drug resistance
Determine conformational differences of the drug in the active site
Calculate drug binding affinities
Mutant 1: G48V (Glycine to Valine)
Inhibitor: Saquinavir
Mutant 2: L90M (Leucine to Methionine)

5. Computational Techniques
Ensemble MD is suited for HPC GRID
Simulate each system many times from same starting position
Each run has randomized atomic energies fitting a certain temperature
Allows conformational sampling
Start Conformation
Series of Runs
End Conformations
Launch simultaneous runs
(60 sims, each 1.5 ns) C1 C2 Cx C3
Equilibration Protocols C4
S.K. Sadiq, S. Wan and P.V. Coveney
Biophys J., (submitted)
eq1 eq2 eq3 eq4 eq5 eq6 eq7 eq8

6. V ∂V/∂ … Thermodynamic integration is ideally suited for a HPC Grid 
Calculating drug affinities
Thermodynamic integration is ideally suited for a HPC Grid t V
Use steering to launch, spawn and terminate - jobs
Starting conformation
Check for convergence
Combine and calculate integral
=0.1
time
∂V/∂
=0.2
=0.3 …
Seed successive simulations
(10 sims, each 2ns)
=0.9 
Run each independent job on the Grid
P.W. Fowler, S. Jha and P.V. Coveney,
Phil. Trans. R. Soc. A., 363, (2005)

7. HPC Requirements
Computational Technique
Total N.o. Simulations Required
Nanoseconds/sim
Total number of cpus for optimal efficiency
Ensemble MD 60
1.5
2560
Thermodynamic Integration 20 2
640
Each Simulation ideally uses 32 cpus (~ atoms)
NAMD - scaled performance currently 8hrs/ns
Simultaneous use of multiple and heterogeneous Supercomputing Resources
Improved policies
resource co-allocation
capability computing vs task farming
EMD and TI are techniques that can really take advantage of a HPC GRID !
But also……
Require suitable middleware to make simulations manageable !

8. Middleware Requirements
Monitoring - Checking resource status and job progression
Optimizing - Determining best resources to launch specific jobs on
Chaining Launching a series of jobs in a sequential chain
Automating - Launching multiple jobs automatically based on definable prerequisites, automatic staging retrieval and clean up
Steering Interacting and changing the direction of simulation during job execution

9. GSISSH vs AHE
GSISSH
Globus installation difficult – does it effect the user ?
Resource awareness
Manual file staging, retrieval and monitoring for each resource
Chaining is simple
Multiple job submission is simple but limited to single resource
AHE
Client can be installed by user – no admin necessary
Does some monitoring, centralized staging and retrieval
Single interface to multiple GRID resources
No queue/resource availability information
Chaining is currently limited
Multiple job submission is simple and can be implemented across multiple resources
Potential for MOCAS

10. Conclusion
Several techniques exist and are still emerging that take advantage of HPC GRID resources
Robust and easy to use middleware assists the scientist to implement these techniques in a feasible manner

11. Acknowledgements Collaborators CCS
Department of Infection & Immunity, UCL
Paul Kellam
Deenan Pillay
Robert Gifford
Simon Watson
MRC HIV Clinical Trials Unit, UCL
David Dunn
CCS
Peter Coveney
Radhika Saksena
Stefan Zasada
Shunzhou Wan
Shantenu Jha
Philip Fowler